Abstract: A system for recognizing abnormal activity of human body using wearable electronic device and mixed reality technology, includes a first wearable electronic device, a plurality of second wearable electronic devices, at least one camera, and a judgement module. The system is suitable to be applied in any type of work environment, so as to monitor and determine whether a user (i.e., an operator or an employee) exhibits abnormal activities by referring a document of standard operating procedures (SOP), an operating manual and a document of safety operation standard (SOS), to reduce a rate of user mishandling, thereby achieving the enhancement of work efficiency and productivity.

Abstract: An image enhancement method includes the following steps. Separating an original image signal into a high frequency image signal and a low frequency image signal by performing a wavelet transform through a first transformation circuit. Separating the high frequency image signal into a high frequency separation signal and a high-intermediate frequency separation signal by performing the wavelet transform through a second transformation circuit. Separating the low frequency image signal into a low-intermediate frequency separation signal and a low frequency separation signal by performing the wavelet transform. Enhancing high frequency separation signal to generate an enhanced high frequency separation signal configured to enhance the sharpness of an image.

Abstract: An image enhancement method includes the following steps. Separating an original image signal into a high frequency image signal and a low frequency image signal by performingperforming a wavelet transform through a first transformation circuit. Separating the high frequency image signal into a high frequency separation signal and a high-intermediate frequency separation signal by performingperforming the wavelet transform through a second transformation circuit. Separating the low frequency image signal into a low-intermediate frequency separation signal and a low frequency separation signal by performingperforming the wavelet transform. Enhancing high frequency separation signal to generate an enhanced high frequency separation signal configured to enhance the sharpness of an image.

Abstract: A multi-microcontroller system, comprising a master microcontroller and a plurality of slave microcontrollers; wherein the master microcontroller is connected with the slave microcontrollers respectively via a network bridge; the network bridge forms a first communication part with a first memory interface and a first SPI interface, the plurality of first communication parts are connected with the master microcontroller and the slave microcontrollers, respectively; the network bridge is primarily responsible for processing transmission of control signals and data between the master microcontroller and the slave microcontrollers, and serves as a temporary storage area for common memory such that the states of the slave microcontrollers are put under automated management, the addresses of the slave microcontrollers are designated, and memory blocks are allocated.

Abstract: A multi-microcontroller system, comprising a master microcontroller and a plurality of slave microcontrollers; wherein the master microcontroller is connected with the slave microcontrollers respectively via a network bridge; the network bridge forms a first communication part with a first memory interface and a first SPI interface, the plurality of first communication parts are connected with the master microcontroller and the slave microcontrollers, respectively; the network bridge is primarily responsible for processing transmission of control signals and data between the master microcontroller and the slave microcontrollers, and serves as a temporary storage area for common memory such that the states of the slave microcontrollers are put under automated management, the addresses of the slave microcontrollers are designated, and memory blocks are allocated.

Abstract: An image interpolation method and an image interpolation device and an image apparatus using the image interpolation method are provided. The image interpolation method uses a probabilistic neural network model to perform an adaptive interpolation on an image. The image interpolation method includes the following steps. Firstly, plural reference points neighboring an interpolation point are selected. Then, an anisotropic Gaussian function value of each reference point of the plural reference points is obtained according to an edge direction angle, a horizontal smoothing parameter, a vertical smoothing parameter and a distance between each reference point and the interpolation point. Afterwards, a statistics method is performed to integrate and compute the anisotropic Gaussian function values of the plural reference points, thereby obtaining an interpolation value of the interpolation point.

Abstract: An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point.

Abstract: An image-capturing system with a dual lens camera provided in the present invention includes a first lens, a second lens, an actuating module, a control unit, and an image processing unit. The actuating module is utilized to rotate the first lens and the second lens. The control unit controls the actuating module to rotate the first lens and the second lens such that a first visibility region overlaps a second visibility region to define an overlapping range which has a first mode and a second mode according to a size of the overlapping region. The image processing unit stitches the first image of the first lens and the second image of the second lens for forming an ultra wide-angle image in the first mode; the image processes unit processing the first image and the second image into a stereoscopic image in the second mode.

Abstract: An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point.

Abstract: An image interpolation method and an image interpolation device and an image apparatus using the image interpolation method are provided. The image interpolation method uses a probabilistic neural network model to perform an adaptive interpolation on an image. The image interpolation method includes the following steps. Firstly, plural reference points neighboring an interpolation point are selected. Then, an anisotropic Gaussian function value of each reference point of the plural reference points is obtained according to an edge direction angle, a horizontal smoothing parameter, a vertical smoothing parameter and a distance between each reference point and the interpolation point. Afterwards, a statistics method is performed to integrate and compute the anisotropic Gaussian function values of the plural reference points, thereby obtaining an interpolation value of the interpolation point.

Abstract: A wireless sensor actuator network (WSAN) is provided, which includes: at least one sensor, for sensing situations of surrounding environments to generate a transferring condition, the transferring condition corresponding to a transferring condition serial number; at least one actuator, driven by a driving signal; and a gateway, for receiving the transferring condition of the sensor corresponding to the transferring condition serial number, transferring an active state serial number to a transferring state serial number which meets the transferring condition according to an encoding table of the gateway, and executing functions called by the transferring state serial number to generate the driving signal for driving the actuator.

Abstract: The present invention discloses an optical input device, which comprises a display panel having a displaying area functioning as an interface for detecting a position of an object; a backlight module providing light sources for the display panel; and at least one image sensor arranged behind the backlight module and capturing a positional image, which is formed on the displaying area by an object reflecting the light emitted by the light sources. The present invention not only can decrease the thickness of the optical input device but also can reduce the complexity of the optical system.

Abstract: A method uses a hybrid neural network including a self organizing mapping neural network (SOM NN) and a, back-propagation neural network (BP NN) for color identification. In the method the red, green and blue (RGB) of color samples are input as features of training samples and are automatically classified by way of SOM NN. Afterwards, the outcomes of SOM NN are respectively delivered to various BP NN for further learning; and the map relationship of the input and the output defines the X,Y, Z corresponding the x, y and z values of a coordinate system of the standard color samples of RGB and IT8. By way of the above learning structure, a non-linear model of color identification can be set up. After color samples are self organized and classified by SOM NN network, data can be categorized in clusters as a result of characteristic difference thereof.